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This article focuses on people excluded from traditional markets as employees, producers, or consumers on the grounds that they lack the appropriate skills. It describes the processes through which these perceived liabilities can be overcome by so-called hybrid organizations. Hybrids pursue explicit social missions through business-inspired earned-income strategies, with the express goal of creating market disequilibria. This article demonstrates the challenges hybrids face and outlines how to overcome them by identifying hidden complementarities and creating new ones, by eliminating the need for complementarities, and by creating demands for antagonistic assets, or by using partnerships.

There is growing recognition that hybrid organizations can play a critical role in tackling intractable global sustainable development challenges. At the same time, acute social, environmental, and economic challenges are opening up "opportunity" spaces for hybrids. Different institutional contexts are also leading to variable hybrid forms linked to the focus of their mission and their profit-oriented status. This article presents a process for identifying, mapping, and building impact indicators based on a study of 20 hybrid organizations in Sub- Saharan Africa.

How do established companies respond to the entry of hybrid social ventures in their industries? Hybrid social ventures-new companies that combine business and social missions-compete with established companies for customers and employees, but also share an interest in the advancement of established companies' sustainability activities, creating opportunities for collaboration. Based on the analysis of eight established consumer-facing companies, this article presents a framework for understanding how these companies' responses to hybrid social ventures differ. These differences are based on opportunities they perceive for sustainability-oriented value creation with their own customers and employees.

After failing to make the basketball, baseball, and football teams in high school, the author persuaded the administration to start a lacrosse team, figuring that no one would know any more than he did about how to play. Eventually he got good at the game and became team captain. He took a valuable lesson from that experience: Opportunity is something you manufacture, not something you wait for. That lesson has informed his career ever since. For example, while he had a part-time job moving boxes as part of a relocation for Little, Brown, he happened across an assignment to create a book jacket. Familiarity with Photoshop and Quark enabled him to quickly come up with a design and slip it into the pile of submissions. When the editors and the sales team chose it as the best, he was offered a full-time job. A couple of start-ups and a stint at Google later, Stone and a close friend came up with the idea for Twitter. His job at the company was to "bring humanity to the technology" and to help "shape our story for the outside world." (He drew the bird logo.) His latest venture is Jelly Industries, a seven-person company of which he's the CEO. There he has learned that the chief executive's most important job is to constantly communicate.

Why is it so hard to build and maintain the capacity to innovate? The reason is not simply a failure to execute but a failure to articulate an innovation strategy that aligns innovation efforts with the overall business strategy. Without such a strategy, companies will have a hard time weighing the trade-offs of various practices-such as crowdsourcing and customer co-creation-and so may end up with a grab bag of approaches. They will have trouble designing a coherent innovation system that fits their competitive needs over time and may be tempted to ape someone else's system. And they will find it difficult to align different parts of the organization with shared priorities. As Corning, a leader in glass and materials science, has found, an innovation strategy must address how innovation will create value for potential customers, how the company will capture a share of that value, and what types of innovation to pursue. Critics tend to discount "routine" innovation that leverages a company's existing technical capabilities and business model and extol "disruptive" innovation, but that is a simplistic view. A company's unique competitive circumstances should dictate the innovation portfolio it pursues. Because innovation cuts across functions, only senior leaders can set an innovation strategy. In doing so, they must recognize that the strategy, like the process of innovation itself, requires continual experimentation and adaptation. HBR Reprint R1506B

People in all walks of life are rightly concerned about advancing automation: Unless we find as many tasks to give humans as we find to take away from them, all the social and psychological ills of joblessness will grow, from economic recession to youth unemployment to individual crises of identity. What if, the authors ask, we were to reframe the situation? What if we were to uncover new feats that people might achieve if they had better thinking machines to assist them? We could reframe the threat of automation as an opportunity for augmentation. They have been examining cases in which knowledge workers collaborate with machines to do things that neither could do well on their own--and they've found that smart people will be able to take five approaches to making their peace with smart machines. Some will "step up" to even higher levels of cognition, where machines can't follow. Some will "step aside," drawing on forms of intelligence that machines lack. Some will "step in," to monitor and adjust computers' decision making. Some will "step narrowly" into very specialized realms of expertise. And, inevitably, some will "step forward," by creating next-generation machines and finding new ways for them to augment the human strengths of workers.

Today's digital innovations are doing for brainpower what the steam engine, and related, technologies did for muscle power during the Industrial Revolution. They're allowing us to rapidly overcome limitations and open up new frontiers, say Erik Brynjolfsson and Andrew McAfee, who have studied the impact of technologies on economies for years. The two MIT professors believe this transformation will create abundance. But they warn that there may be a dark side: Though the pie will get bigger, not everyone will benefit equally. As computers get more powerful, companies have less need for some kinds of workers. That shift is contributing to a phenomenon the two academics call the Great Decoupling: For decades, per capita GDP, productivity, private employment, and median family income rose in almost perfect lockstep. But in the 1980s, growth in income began to sputter and then began to drop. Adjusting for inflation, the median U.S. household today earns less than the median in 1998 did. Job growth has also slowed. Similar trends are emerging in most developed countries. In this interview, Brynjolfsson and McAfee explore the implications: who will win (workers with tech and creative skills), who will lose (the middle class), and how business should respond to the coming tech surge (develop ways to race with machines, not against them).

Wouldn't it be great if there were an algorithm that could tell you when to develop a new business model or enter an emerging market? Unfortunately, one doesn't exist. However, it is possible to use the principles behind algorithms to continually retune your strategy and your organization. In online enterprises, algorithms constantly readjust the products and content shown to customers. They do this by operating three learning loops: experimentation, modulation, and shaping. Algorithms keep generating new options and testing reactions to them. But over time the algorithms modulate the rate of experimentation, scaling it back as they learn more about people's likes and dislikes. Algorithms shape preferences too, by introducing customers to products they might not have otherwise discovered. Critically, they do this all in a self-directed manner, without any human intervention. Now some internet companies have begun to regularly readjust their business models, allocation processes, and structures using the same self-directed learning loops. This approach works especially well in rapidly changing markets, like China. In this article, the authors look at how China's Alibaba grew from an 18-person start-up to an $8 billion empire by regularly resetting its vision, testing out new models, shaping opportunities, and building adaptive structures.

As machines evolve from tools to teammates, the author writes, accepting them will be more than a matter of simply adopting some new technology. The first challenge will be recognizing when computers know more than we do. "Algorithm avoidance," for instance, makes people prefer human judgment over that of machines and can lead to worse decisions, from diagnosing patients to predicting political outcomes. The message for managers is that helping humans to trust thinking machines will be essential. One way to encourage that trust is to make robots more humanoid. Researchers at Carnegie Mellon explored this idea with an autonomous robot named Snackbot, which had wheels, arms, a male voice, and an LED mouth that could smile and frown. People in the office made conversation with it and treated it with kindness. But this approach may lead us to put too much faith in the machines' abilities. How we work with thinking machines will vary according to the work we're doing, how it's framed, and how the machines are designed. But under the right conditions, people are surprisingly open to a robotic colleague.

Companies may want to shift a brand's position--to exploit less crowded territory, for example, or grow sales. Companies have long used perceptual mapping to understand how consumers feel about their brands relative to competitors', to find gaps in the marketplace, and to develop brand positions. But the business value of these maps is limited because they fail to link a brand's market position to business performance metrics such as pricing and sales. Other marketing tools measure brands on yardsticks such as market share, growth rate, and profitability but fail to take consumer perceptions into consideration. In this article, Ivey Business School's Niraj Dawar and Charan K. Bagga present a new type of map that links a brand's position to competitors according to its perceived "centrality" (how representative it is of the company) and "distinctiveness" (how much it stands out from other brands) with its business performance along a given metric. Using the tool, marketers can determine a brand's current and desired position, predict its marketplace performance, and devise and track marketing strategy and execution. In-depth examples of the car and beer markets demonstrate the value of this tool to managers of brands in any category.

Fifty years ago fashion and luxury goods were all about family businesses and entrepreneurial designers. Today most of the best-known brands belong to a diversified group like LVMH, Richemont, or Kering. The success of such groups contradicts the management notion that a focus on one core leads to superior returns--and that diversification adds little value. An analysis of the performance of 350 fashion houses suggests that group affiliation does add value, say INSEAD's Andrew Shipilov and Frederic Godart. They found that the fashion collections of group brands were more successful, and judged more creative by industry buyers, than those of independent brands over a 10-year period. How do luxury groups get this edge? Through the way they groom their designers and managers. The groups exploit the variety of their businesses by setting up internal talent markets that offer rich learning opportunities. Rising stars are thus less tempted to leave the organization, and cross-group job moves help spread knowledge and best practices. Group watchmaking companies, for instance, have been able to boost their sales by hiring experts in marketing and CRM from their groups' fashion and cosmetics firms. Worldwide operations give groups another advantage, because spending time overseas is critical to executive development. This is especially true in the luxury field, where international experience exposes designers to creative influences and executives to new but fast-growing customer segments. However, it isn't just in their approach to internal talent that groups excel. They prime the entry-level pipeline by sponsoring educational programs and offering apprenticeships to promising students, as well as recruit external functional know-how more effectively than the independent brands do.

Many people today regard their computers, tablets, and smartphones as indispensable, both professionally and personally. But they can also be dangerous: Research shows that spending too much time on e-mail and social media reduces your productivity and engagement at home and at work. So how can you avoid digital overload? In this article, two experts offer dramatically different strategies. Larry Rosen, a psychologist, advocates pulling away from technology in order to refocus. His advice: Limit the time you spend with your devices, using behavioral techniques to gradually wean yourself away; when you are using technology, take periodic breaks to exercise, meditate, or otherwise recharge; and avoid on-screen activities before bedtime to ensure that you get proper rest. Alexandra Samuel, a pioneer in electronic engagement, recommends the opposite tack: Embrace technology and make smarter use of digital tools to manage information overload. She suggests filtering your e-mail and using a newsreader app to customize the stream of articles and blog items you receive. You can also automate your postings to social networks. The author's approaches needn't be mutually exclusive. Indeed, combining them may be the best way to combat digital distraction.

The politician and peace negotiator on starting a dialogue, getting people to listen, and the importance of public service.

When researchers asked carpenters, roofers, and in-line skaters how to improve safety gear for all three activities, each group turned out to be significantly better at thinking of novel solutions for the fields other than its own. Studies like this one demonstrate the value of accessing expertise from "analogous fields"--areas that seem different on the surface but are similar on a deep structural level. This piece shows how to find the far-flung experts who can jump-start your search for breakthrough ideas. This reprint contains two additional Idea Watch articles. "Global Growth, Paltry Returns" describes new research showing that many international expansions fail to deliver consistent and sizable profits and that companies are often better off growing locally. "When to Forget the Rearview Mirror" examines a study of forecasts in the music business, showing that in creative, volatile fields, historical data often impairs people's ability to interpret contextual data and make sound predictions.

Though the Apple logo is ubiquitous, 99 out of 100 people can't draw it from memory. The reason? Our brains don't burden us with information we don't think we'll need.

Charts that show a close correlation are often relying on a visual parlor trick. Tyler Vigen, a Harvard Law student, makes fun of them on his website.

Despite spending US$50 million to land one of the most respected coaches in National Hockey League (NHL) history, the Toronto Maple Leafs — an iconic team with abysmal performance — are once again setting fans up for disappointment. If the goal is to increase compensation levels for the fraternity of hockey coaches, then the Leafs have succeeded. But if the objective is to bring the Stanley Cup back to Toronto, the data does not support importing a proven winner. In fact, if Babcock wants to add another Cup to his impressive record, the odds say he should have stayed with the Detroit Red Wings. It is easy to be seduced by the glitter of a Stanley Cup ring and to argue for the proven-winner theory. But as this article explains, the Leafs have had proven winners with former coaches Burke and Carlyle and have failed to achieve the desired results.

Many early adopters of data mining wasted millions of dollars due to the significant amounts of dirty data contained in IT systems and the length of time required to build data warehouses. But the potential payoff of data mining has soared along with the growth of digital data. Today, thanks to the massive amounts of user-generated content online, firms do not need to build a data warehouse to benefit from data mining. Mining social media sites for actionable intelligence should be on the radar of consumer product businesses. This article examines research that has helped develop an innovative, high-validity method of mining online product reviews such as those on Amazon.com. Firms can now learn what customers consider the strengths and weaknesses of their products and competitors’ products — right from the horse’s mouth — without conducting costly surveys or focus groups.

Our increasing capacity to collect, store, and analyze large volumes of data has changed the way in which organizational decision makers approach their work. The ability to accurately quantify variables that previously had been assigned to the gut instinct of grizzled veterans or subject to the wisdom of sages for interpretation can now be more objectively understood. The implications for organizational performance are clear: better data and better decisions yield better performance. In many functions, like marketing, this capability has resulted in a true revolution in how companies come to understand and most profitably serve customers. Other areas, such as talent management, have lagged behind in this regard. This is largely due to the fact that many of the relevant variables (e.g., personality) are difficult to measure. It is also because the relationship between these variables and organizational performance is not entirely understood. Recent developments regarding how we understand and then link individual characteristics and performance are enabling a data revolution in the area of talent management. Herein, we offer three examples that illustrate how data can now be used to improve talent management decisions and, ultimately, organizational performance.

Should your company become a benefit corporation? In a comprehensive set of law review opinions, this installation of Business Law & Ethics Corner uncovers several fundamental issues to consider. First, the main premise for the benefit corporation--the legal preeminence of the shareholder primacy norm--may be unfounded. Second, benefit corporations may increase director liability and company costs. Third, contrary to the stated goal of such laws, benefit corporations do not empower stakeholders, and therefore are not substantially different from traditional corporations. Many legal analysts argue that, paradoxically, benefit corporations actually inhibit corporate social responsibility efforts by perpetuating the myth that business corporations do not have the flexibility to pursue social missions, and by claiming to, but failing to, empower stakeholders. They argue that the benefit corporation form is likely to increase corporate greenwashing, and that it enhances public cynicism about all corporations by creating competing sets of 'beneficial' and 'other' corporations. In the face of widespread acclaim for the benefit corporation, both corporate directors and researchers should take these significant concerns into account.